Use tools in prompts

Tools let your LLM interact with external services, databases, and APIs during a conversation. When a model determines it needs external data or actions, it generates a structured tool call request that can be executed to fetch results and continue the conversation. Adaline supports the full tool calling workflow — from defining tool schemas to configuring automatic execution with custom backends.

Add tools to a prompt

Select a compatible model

Choose an LLM that supports tool calling (function calling). Most modern models from OpenAI, Anthropic, and Google support this feature.

Write your prompt

Compose a prompt that may require external data or actions. For example, a prompt asking about current weather conditions would benefit from a weather API tool.

Enable tool choice

Enable the tool choice feature in the model settings to allow the LLM to generate tool calls.

Configure tool choice mode

Set the tool choice mode to control how the model uses your tools.

Choose from the following modes (availability varies by model):

Mode	Behavior
none	The model will not invoke any tools.
auto	The model decides which tools to use and when, based on the conversation context.
required	The model must invoke at least one tool in its response.
any	The model can call any of the available tools.

Define or link a tool

Click Add Tool to create a new inline tool definition, or link to an already defined tool in the project.

Tool schema definition

Each tool is defined using a JSON schema that tells the model what the tool does and what parameters it accepts. Click Add Tool to open the schema editor:

Here is the complete JSON structure for a tool definition:

{
  "type": "function",
  "definition": {
    "schema": {
      "name": "get_weather",
      "description": "Get the current weather in a given location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA"
          },
          "unit": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"]
          }
        },
        "required": ["location"]
      }
    }
  }
}

Schema reference

Field	Type	Required	Description
`type`	String	Yes	Always set to `"function"`.
`name`	String	Yes	A unique identifier for the tool.
`description`	String	Yes	Describes what the tool does. Helps the model decide when to use it.
`parameters.type`	String	No	The parameter structure type. Typically `"object"`.
`parameters.properties`	Object	No	Defines individual parameters with their types and descriptions.
`required`	Array	No	Lists the parameter names that are mandatory.

Refer to the OpenAI function calling documentation for more schema examples and best practices.

Add tool calls and responses to messages

Beyond defining tools, you can add tool call and tool response messages directly in the Editor. This is useful for building multi-shot prompts that demonstrate how the model should interact with tools.

A tool call (in an Assistant message) represents the model invoking a specific tool. A tool response (in a Tool message) shows the data returned by the external service. Together, they teach the model the expected interaction pattern.

Configure auto tool calls

For tools that connect to a live backend, you can configure an HTTP request endpoint so the Playground automatically executes the tool and continues the conversation. Add a request object to your tool definition:

{
  "type": "function",
  "definition": {
    "schema": {
      "name": "get_weather",
      "description": "Get the current weather in a given location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA"
          }
        },
        "required": ["location"]
      }
    }
  },
  "request": {
    "type": "http",
    "method": "get",
    "url": "https://api.my-tool-backend.com/get_weather",
    "headers": {
      "Content-Type": "application/json",
      "Authorization": "Bearer <your-api-key>"
    },
    "retry": {
      "maxAttempts": 3,
      "initialDelay": 1000,
      "exponentialFactor": 2
    }
  }
}

Request configuration reference

Field	Type	Required	Description
`type`	String	Yes	Always set to `"http"`.
`method`	String	Yes	The HTTP method (`GET` or `POST`).
`url`	String	Yes	The endpoint URL to call.
`headers`	Object	No	HTTP headers to include in the request.
`retry`	Object	No	Retry configuration for failed requests.
`retry.maxAttempts`	Number	No	Maximum number of retry attempts.
`retry.initialDelay`	Number	No	Initial delay in milliseconds before the first retry.
`retry.exponentialFactor`	Number	No	Multiplier for exponential backoff between retries.

When auto tool calls are enabled and a tool has a configured request endpoint, the Playground will automatically invoke the tool, inject the response, and continue the conversation — enabling fully automated multi-turn interactions.

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Use tools in prompts

Add tools to a prompt

Tool schema definition

Schema reference

Add tool calls and responses to messages

Configure auto tool calls

Request configuration reference

Next steps

Tool Calls in Playground

Use MCP Servers in Prompts

​Add tools to a prompt

​Tool schema definition

​Schema reference

​Add tool calls and responses to messages

​Configure auto tool calls

​Request configuration reference

​Next steps

Tool Calls in Playground

Use MCP Servers in Prompts

Add tools to a prompt

Tool schema definition

Schema reference

Add tool calls and responses to messages

Configure auto tool calls

Request configuration reference

Next steps